Performance evaluation of experimental setups in

home energy management systems in smart grid

W. Naeem1, N. Javaid1,∗, Z. A. Khan2, U. Qasim3,

A. Mahmood1, Z. Mehmood1, A. Ahmad1, F. Ali4, W. Sarwar4

1COMSATS Institute of Information Technology, Islamabad, Pakistan2CIS, Higher Colleges of Technology, Fujairah Campus, UAE

3University of Alberta, Alberta, Canada4Grafton College, Islamabad, Pakistan

∗www.njavaid.com, nadeemjavaid@comsats.edu.pk, nadeem.javaid@univ-paris12.fr

Abstract—Now a days, smart grid is an emerging technologyto increase the optimal use of generated power and reducethe electricity cost. Globally, various schemes in this aspecthave been proposed like DR, DSM, AMI, RTP, TOU, etc. Thispaper presents a survey on some scheduling and optimizationtechniques for home appliances to use the energy efficiently with aminimum cost. The focus of this paper is on experimental resultsand test beds of different schemes to evaluate their performance.This paper also presents critical analysis on the Home EnergyManagement (HEM) scheduling schemes based on their results.

Index Terms: Smart grid, optimization techniques, energymanagement system, DSM, TOU, RTP, IBR.

I. RELATED WORK

In [1], authors focus on techniques for reducing the energy

consumption and peak electricity demands. They also review

the communications and network technologies for HAN, for

interworking the HEM to end points and smart meters. Authors

survey different network technologies as home plug, X10,

Z-wave, EnOcean etc. They also discuss the software and

hardware tools, platforms and test beds for evaluating the

performance of information and communication technologies.

N-Javaid et al. [2] present a review of various HEM

schemes that have been proposed that lead towards efficient

consumption of electricity and reduce the users bill. Different

schemes are discussed in this paper, such as Optimized Based

Residential Energy Management (OREM), In-Home Energy

Management (iHEM), appliance coordination, optimal and

automatic residential energy consumption scheduler. It also

surveys general HEM systems architecture and challenges to

implement this architecture in smart grid.

Authors present a survey in [3] on DR potentials and

benefits in smart grid. Innovative enabling technologies and

systems such as smart meters, energy controllers and commu-

nication systems are described and discussed with reference to

real industrial case studies and research projects. Technologies

such as smart meters, AMI, home energy controllers, EMS,

wired and wireless communication systems are required to

improve the reliability of the power system and lowering the

peak demand. Demand response (DR) offers a broad range

of potential benefits on system operation and reduces overall

plant and capital cost investments and postpones the need for

network upgrades.

Siano, P. et al. [4] mainly focus on DSM and DR, shiftable

load scheduling methods and peak shaving techniques. Using

new techniques, grid capacity, efficiency, reliability, power

quality, sustainability and lowering the carbon foot can be

improved. All of the discussed DSM techniques are based

on load shifting technique. Most of them use scheduling

algorithms to optimize the objective demand profile.

The background and motivation of communication infras-

tructures in smart grid systems is presented in [5]. A smart grid

built on sensing, communications, and control technologies

offers a very promising future for utilities and users. Through a

communication infrastructure, a smart grid can improve power

reliability and quality to eliminate electricity blackout. The

consumers can minimize their expenses on energy by adjusting

their intelligent home appliance operations to avoid the peak

hours and utilize the renewable energy instead.

II. BACKGROUND: ELEVEN HEM TECHNIQUES IN BRIEF

In this section, we briefly discuss the seven selected existing

HEM techniques.

A. Demand Response Management via real time pricing

In [8], a real time pricing technique is presented that

helps to reduce peak to average ratio by managing DR. Two

algorithms are proposed; one is to implement the electricity

consumption scheduling and other is implemented at retailer

side. It supports the consumer economically to maximize the

payoff and helps the retailer to design the real time prices to

maximize the profit.

B. An intelligent home energy management system to improve

demand response

Authors in [9], propose an integrated solution to curtail

the use of energy during peak hours. The presented system

includes ANFIS model to predict and a branch and bound

based appliance scheduler. Predictor predicts the optimal run

time while scheduler schedules and controls the residential

loads to save the huge amount of energy.

C. Modeling for Residential Electricity Optimization in Dy-

namic Pricing Environments

Hubert et al. [10] focus on scheduling home appliances,

home grid components and storing of power in dynamic

pricing environment. It provides solution to optimize the house

hold energy consumption according to users preferences. This

helps the user in increasing the huge savings.

D. An Optimal Power Scheduling Method for Demand Re-

sponse in Home Energy Management System

Authors in [11] describe general architecture of Energy

Management System (EMS) and then present an efficient

scheduling scheme to reduce the electricity cost and Peak-

to-Average Ratio (PAR). If we use Real Time Pricing (RTP)

model, it just only reduces the electricity cost but PAR

becomes high. The proposed scheme uses the RTP model,

combined with Inclined Block Rate (IBR) model. Using this

combined model, the electricity cost as well as PAR is reduced.

E. Genetic-algorithm-based optimization approach for energy

management

An optimized technique to increase the efficiency of hy-

brid system (renewable energy and energy storage) to meet

the controllable Heating, Ventilation and Air Conditioning

(HVAC) load is presented on [12]. In order to minimize the

cost of Photo Voltaic (PV) and wind generation, Genetic-Based

(GA) optimization approach is used. Minimizing cost function

shows minimum PV and wind generation installation.

F. An algorithm for intelligent home energy management and

demand response analysis

Pipattanasomporn et al. [13] present an intelligent HEM

algorithm for DR applications. Proposed HEM algorithm

schedules and effectively controls the appliances to keep the

total household consumption below the demand limit.

G. Demand side management in smart grid using heuristic

optimization

The proposed DSM scheme is a day-ahead load shifting

technique that can be applied at each type of area (residential,

commercial and industrial). Suggested DSM strategy is used

to reduce the energy consumption and provide the benefits not

only to the end user but also to the retailers by reducing PAR.

H. Autonomous Appliance Scheduling for Household Energy

Management

Authors in [15] address the challenge of automation of DR.

This automation is done by accurate load forecasting. For

this purpose, new approach of predicting the hourly energy

demand of appliances, is proposed. Further, depending on

predicted load, load clustering technique and energy pricing

frameworks are also been introduced. This work also presents

the DSM technique to use the energy below the specified limit.

For efficient energy consumption, smart scheduling of home

appliances has also been proposed.

I. Optimal Power scheduling

Zakaria Ziadi et al. in [16] propose an optimized schedul-

ing technique for Distribution Grids (DGs), Battery Energy

Storage Systems (BESSs) and tap changing transformers. The

integration of Control Loads (CLs) has also been presented.

The main purpose of this paper is to reduce the distribution

losses and to prevent the reverse flow of voltage towards

substation. This objective is achieved using Particle Swarm

Optimization (PSO).

J. Appliance Commitment for Household Load Scheduling

Appliance commitment algorithm has been presented in [17]

to optimally schedule the thermostatic appliances to reduce

the energy cost or increase the user comfort. This algorithm

is designed by modeling electric water heater as an example.

Two step scheduling process has also been proposed. First

step is scheduling the day-ahead loads based on forecasted

prices and second step is real time adjustment. This two

step process enables the adjustments in scheduling because

of uncertainty produced by forecast error in prices and con-

sumption. Optimization process is: Step1: Obtain a forecasted

day-ahead price curve. Step2: Estimate the duration of water

heater needs to be on to warm up the water in tank. Step3:

Find the price depending on duration of ON time of water

heater. Step4: Check whether the comfort limits are violated

or not. If violation occurs at instant t1, subdivide the time

interval into [t0, t1] and [t1, 24] Step5: Repeat the process

for new interval until comfort level occurs and then find the

total price. (See the detailed discussion on this mode, given

in paper)

K. Management and Control

Three step control technique is proposed in [18] to manage

the energy production and utilization efficiently. The control

strategy includes local prediction, global planning and real

time scheduling(control). The main motive of this work is to

optimize the efficiency of current grid, support the penetration

of renewable sources and use of energy efficiently.

III. PERFORMANCE EVALUATION OF EXPERIMENTAL

SETUP OF CHOSEN HEM TECHNIQUES

In this section, we discuss the performance of selected HEM

schemes. Simulations are presented to show the performance

of HEM techniques.

A. Demand response management via real-time electricity

price control in smart grids

Authors assume that 100 users are attached with a smart grid

where each user has 4 elastic and 2 semi-elastic appliances.

The time scheduling horizon is H = {1, 2, ..., 12}.Proposed Simulated Annealing based Price Control (SAPC)

algorithm is being used to compute the optimal flat rate price

and optimal real time price. Fig. 1 depicts that real time

pricing scheme reduces the peak to average ratio by 20% as

compared to flat rate pricing scheme. It is clear from figure that

increase in electricity price leads to reduction in total energy

consumption.

Fig. 1. The total energy consumption under different settings of price.

B. An intelligent home energy management system to improve

demand response

The proposed ANFIS model and a branch and bound based

appliance schedular are used for simulation results by using

Time Of Use (TOU) pricing for home energy management

system.

1) Prediction: ANFIS model is used to predict the ap-

pliance switching ON time and its duration of operation.

It is then divided into two models. First model is used to

predict the appliance switching ON time and the other model

predicts its duration of operation. The data of five weeks

was used for training and testing the ANFIS model. Fig. 2

shows the prediction results over one week for two schedulable

appliances; air conditioner and washing machine.

Fig. 2. Prediction result for home appliance usage for (a) air conditioner, and(b) washing machine. Here, blue line and red line represent the patterns forgenerated data and the predicted results, respectively.

The result shows that ANFIS model is useful in predicting

the home appliance usage pattern.

2) Scheduling: Assume that we want to schedule some

home appliances. The peak hours are from 7:30 P.M. to 7:00

A.M., the time between 7:00 A.M. to 2:00 P.M. is normal

and 2:00 P.M. to 7:30 P.M. are off-peak hours. Fig. 3 shows

the graphical representation of power availability and cost of

electricity during different times of the day.

The branch and bound algorithm, applied to above schedul-

ing problem, gives the optimal time of operation for appliances

which are 8:00 A.M., 9:08 A.M. and 10:30 A.M. for dish

washer, washing machine and dish dryer respectively.

Fig. 3. An example of power availability and cost of electricity duringdifferent times of the day used in simulation.

C. Modeling for Residential Electricity Optimization in Dy-

namic Pricing Environments

Different scenarios and their simulation results are discussed

in this section. A simulation scenario consists of home grid

congiguration, number of retailers and a proposed algorithm.

All scenarios are simulated for time horizon D = 24h with

τ′

= 5min and τ = 2.5min. The price signals used are the

actual price signals provided by the retailer.

For flat price scenario, assuming the electricity

provider(µ = 1), reference case is compared with optimized

case. Under optimized case, the objective function C is

$12.16 with PAR 2.07 and under reference case C = $12.38and PAR 4.02. This shows that optimized case outperforms

the reference case.

In scheduling controllable loads, the economic gain varies

from 9.8% to 28.3%. If Energy Storage System (ESS) is

present in house, the proposed method outperforms the ref-

erence case by 21.2% to 37.7%. If consumer has a solar panel

connected to his house, the proposed method improves the

objective function by 34.9% to 86.6%. Similarly, if the option

of selling power to Point of Common Coupling (PCC) is

available the proposed method advances the objective function

by 23.3% to 53.3%.

1) Comparison with perfect forecasts: The proposed

method reduces uncertainty of cost by comparing traditional

non-controllable consumptions with day-ahead power genera-

tion. In fact, there is less probability that the actual value of

cost will be greater than proposed value of algorithm.

The proposed algorithm notably reduces the PAR as com-

pared to Reference case Real-Time pricing (R-RT) and Refer-

ence case Day Ahead pricing (R-DA) because of better control

scheme of HVAC system and pricing mechanism.

D. An Optimal Power Scheduling Method for Demand Re-

sponse in Home Energy Management System

This paper proposes the scheme to reduce the electricity

cost and PAR. The simulations are carried out to present the

performance of proposed scheme for home appliances power

scheduling.

1) Impact Of AOAs: in this paper, authors consider nine

kinds of Automatically Operated Appliances (AOAs) and they

are 16 in numbers.

Electricity cost and Delay time Rate (DTR) are inversely

proportional to each other. The simulation results in fig 4

shows that when the value of DTRavg increases, the value

of electricity cost decreases.

Fig. 4. Trade-off between electricity cost and average delay time rate.

From figure, at DTRavg=0, the major consideration is to

minimize time delay so in this case w1 = 0 and w2 = 0 and

that for minimum electricity cost, w1 = 0 and w2 = 0.Impact of Inclined Block Rate:

In this section, we focus only on minimizing electricity cost.

We consider comparison of electricity cost and PAR with and

without the proposed approach. The fig. 5 leads us to the result

that proposed scheme reduces both the electricity cost and

PAR. By applying this scheme, the average daily electricity

cost of 48.67 cents with PAR=5.22 decreases to 35.97 cent

with PAR=3.37.

Simulation result shows that proposed RTP combined with

IBR is a better way to reduce PAR. The proposed scheme also

performs good in eliminating peak power demand. Two power

consumption profiles are taken to demonstrate the effectiveness

of RTP combined with and without power scheduling based

on RTP are shown in fig 6.

2) Impact Of MOAs: The proposed scheme is also effective

for Manually Operated Appliances (MOAs) operations. Using

this scheme, the average electricity cost reduces from 65.11

cents with PAR=4.26 to 55.01 cents with PAR=3.42 as shown

in fig. 7. These results for MOAs and AOAs show that pro-

posed approach is always effective for residential customers.

E. Genetic-algorithm-based optimization approach for energy

management

Authors examine two scenarios for an energy management

system. The first is simplified and deterministic and other is

stochastic.

1) Scenario I: It corresponds to deterministic wind gener-

ation, PV generation and cooling load for a residential feeder

over a single day. Two different cases are studied using GA-

based optimization.

Fig. 5. The impact of the proposed power scheduling approach on: (a) dailyelectricity cost and (b) PAR.

Fig. 6. The impact of IBR in the proposed approach on PAR.

Case Study 1:

The simulation results in fig. 8 show that in the absence

of load shifting, the modified cooling load is the same as the

cooling load during hours 1− 15. Storage shows the state ofcharge of the storage system at each hour. As shown in figure,

wind generation is always included in the optimal solution

since wind installation cost per megawatt is less than PV

installation cost. The storage system is charged in the early

hours of the day when more energy is available and the load is

low. It releases the stored energy stored when the load exceeds

generation. During peak hours when the wind is low and the

storage is not sufficiently charged, load shifting dispatches the

Fig. 7. The impact of MOAs operations on: (a) daily electricity cost and (b)PAR.

flexible loads to hours when the wind energy is in excess of

the load.

Fig. 8. Simulation result for Case Study I (Scenario I).

For this case study, the optimum PV, wind installation, and

storage capacity are 0.0, 1.9, and 2.92 MWh respectively. Total

installation cost of the system is $4.615× 106 ($3.80× 106 forwind and $0.815× 106 for storage).

Case Study 2:

This case study evaluates the possibility of matching HVAC

load and PV plants, which is particularly important for regions

with poor wind speed profiles and an abundance of solar

energy. The simulation results show that by installing more PV

capacity and storage capacity, the load can be fully supplied

without wind energy generation.

2) Scenario II: It corresponds to stochastic models of wind

generation, PV generation, and cooling load. In this scenario,

two cases are presented.

Case Study 1:

The GA-based optimization problem is solved by con-

sidering the probabilistic PV generation, wind generation,

and cooling load. Fig. 9 shows the cumulative distribution

functions FS(s) of the storage capacity(S) for each Load

Shifting (LS).

Fig. 9. Cumulative distribution of the maximum capacity of the storage systemfor different LS percentages (Scenario II, Case Study I).

Case Study 2:

This case study evaluates the possibility of matching HVAC

load and PV plants for regions with poor wind speed profiles

and an abundance of solar energy. For both S and EE, the

cumulative probabilities increase with LS. Thus, increasing the

LS from 10 to 50% provides the system with more flexibility.

F. An algorithm for intelligent home energy management and

demand response analysis

Simulations show that proposed scheme control and man-

age the appliances operation to keep the total house hold

consumption below the given demand limit. To demonstrate

the efficiency of proposed approach, a house and its different

parameters are considered.

In this paper, authors assume that during peak period (5

P.M.-10 P.M.), a demand limit is imposed on this house.

Different demand limits(8kW, 6kW, 4kW) are considered to

test the efficiency of proposed scheme. Simulations are taken

from [7]. Fig. 5(b)-(d) show the simulated results for 8kW,

6kW and 4kW demand limit respectively, during peak hours.

It shows that proposed HEM effectively controls the ap-

pliances to keep the total household consumption below the

demand limit. However, if demand limit is lower than the total

household consumption then user has to sacrifice the comfort

levels. On the other hand, a low demand limit may result in

creating a high peak during an off-peak demand when DR

ends.

Here it is significant to know the lowest possible demand

limit level that can be assigned to a house before any violation

occur. The lowest possible demand limit for a house depends

on rated power (kW), appliance usage pattern, comfort level

settings, house parameters and duration and start time of

DR event. Authors are interested to find the lowest possible

demand limit before comfort level violation or high load con-

sumption occurs in different scenarios. See table for different

simulation scenarios.

The DR simulations for case 1 and 3 are taken from [7]. For

case 1, no violation occurs at demand limit of 8kW, however,

when the demand limit is below 7kW, high load compensation

is offered at DR event.In case 3, the lowest possible demand

limit must be atleast 8.6kW. If the demand limit is below 8.6

kW, comfort level violation can occur. However, for 9kW no

comfort level violation occurs.

G. Demand side management in smart grid using heuristic

optimization

Simulations are carried out to show that proposed algorithm

has efficiently managed the large number of controllable loads.

It also brings reduction not only in utility bills but also in peak

load demand.

1) Effect On Utility Bills: The simulation results obtained

for residential area is shown in fig. 10. After applying the DSM

strategy, the saving in utility bills for these three types of areas.

The reduction in bills for industrial area is comparatively high

because even small load shifting of high power devices results

in huge saving for customers.

Fig. 10. DSM results of the residential area.

2) Effect On Peak Load Demand: The DSM scheme re-

duces peak load demand for each area. Reduction in peak

load demand not only improves grid sustainability but also

increases cost savings for generation companies. When load

demand reduces, the operating cost of generators reduces and

this increases the reserve generation capacity of system.

H. Autonomous Appliance Scheduling for Household Energy

Management

In this section, evaluation of proposed DSM scheme is pre-

sented. Set of twenty two home appliances is used to simulate

the daily energy consumption of a house. As discussed in [1],

three different pricing frameworks are considered to evaluate

the performance of presented scheme.

Case1-Real Time Pricing: In this case, those customers

are considered who have not installed any microgrid at their

home. Therefore, they totally depend on main power grid to

fulfill their home energy requirements. They can only save

the billing cost by consuming the energy efficiently. Fig. 11

shows the results of unscheduled load energy cost under flat

and dynamic pricing systems.

Fig. 11. Unscheduled load energy cost.

Fig. 12. Scheduled load energy cost.

It is clear from figure 11 that real pricing scheme is costly

than flat rate system by 6.7%. On the other hand, dynamic

tariff scheme is cheaper than flat rate pricing method by 8.54%

during peak hours (fig 12). Under real time pricing, a further

comparison between unscheduled and scheduled loads reveals

that 10.92% savings can be made by scheduling loads.

Case2- Feed In Tariff (FIT): Under FIT plan, utility

company offers a purchase agreement to the producers of

PV energy for all the energy they produce. For simulation,

we assume that a house purchases power from the retailer

at 0.13/kWh while it sells the PV energy to main grid at

0.15/kWh. After calculations, 9.43% of cost saving can be

made under FIT as compared to grid dependent customer.

The authors also considered two scenarios in this case. First

scenario corresponds to household that uses produced PV

power without scheduling its loads. Fig. 13 shows the first

scenario energy cost under FIT plan. It can be seen that energy

bill is 4.162% lower than that of traditional customer (grid

dependent). Second scenario corresponds to household that

uses generated PV energy with scheduling its loads. Fig. 14

shows that energy cost is lower as compared to first scenario

and 5.9% lower than that of grid dependent customer.

Fig. 13. Unscheduled load energy cost under FIT plan.

Fig. 14. Scheduled load energy cost under FIT policy.

Case3- Net Purchase and Net Sale: In this arrangement,

the PV production and household power requirement are com-

pared instantaneously. For simulations, they are compared on

hourly basis. Like previous cases, Energy cost for scheduling

and non-scheduling loads are observed in this case. Fig. 15

shows the load scheduling energy consumption case. Positive

values show the purchased energy while negative values are

for PV sold to grid. The comparison between energy costs of

net sale/net purchase, flat rate and dynamic pricing scheme

is presented in fig. 16. It is clear that net purchase/net sale

pricing is cheaper than dynamic pricing by 8.967% and the

flat pricing plan is higher than that of real time pricing by

9.33%. Fig. 17 shows the energy cost for unscheduled load

consumption under net sale/net purchase plan. It is evident

that this plan still benefits the customer than both others. In

this case, real time pricing is lower than flat rate by 8.607%.

Fig. 15. Unscheduled load energy cost.

Fig. 16. Scheduled load energy cost under net purchase and net sale plan.

Fig. 17. Unscheduled load energy cost under net purchase and net salescheme.

I. Optimal Power scheduling

In order to demonstrate the effectiveness of proposed

scheduling method and effect of integration of CLs in SG,

three cases have been studied which are as follow:

Case1: This case study refers to scheduling of tap positions

of transformers to reduce distribution losses and optimize the

voltage within the possible range. Fig. 18 shows the simulated

results of this case. A voltage rise occurs in the system due to

huge PV generation and a voltage drop at night due to peak

demand as shown in fig. 18a (dashed lines show the maximum

and minimum limits of variables). As tap transformers could

not follow the voltage variations, so they decrease slowly at

day times and then increase at night to cover the voltage drop.

Fig. 18(b) shows that reverse power flow occurs due to PV

generation at day times.

(b)

(a)

Fig. 18. Simulation results of case 1. (a) Node voltages. (b) reactive powerflow at connection point..

Case2: This case study involves the reference schedule

optimization for DGs, BESSs and tap transformers. However,

this study does not include CLs. The simulation results are

taken from [16] (fig. 8). The node voltages in fig. 8(a) are

within the specified range despite of high voltage generation at

day time. Fig. 8(c) shows that tap positions of LRT and SVRs

are optimized which means they are controlling the voltages

and hence reducing the distribution losses. Figs. 8 (d and e)

show the active and reactive powers at connection point. In the

figure, no violation occurs and flow is smooth which indicates

no reverse flow towards substation. This reduces the harm to

substation transformer. Fig. 8(f) shows the active and reactive

power of BESS. It absorbs the excessive power produced

by the DGs, maintaining the node voltage and opposing the

reverse power flow.

Case3: This case study corresponds to optimization of DGs,

BESSs and tap transformers considering CLs, which means

DR is applied to the system in this case. The simulation

results are taken from [16] (fig. 9). The fig. 9a shows the

optimized node voltage. However, voltages in this case are

more concentrated as compared to case 2. This increase in

voltage can affect the substation. The active and reactive

powers of DGs are smooth like in case 2 and no reverse flow

towards substation occurs. Thus, the optimization constraints

are totally satisfied within the applied bounds.

Fig. 19 shows the hourly total power distribution for case

2 and 3. The loss in case 2 is greater than case 3 at night

because of CLs in curtailing the load demand in case3.

Fig. 19. Total power losses of cases 2 and 3.

Authors also considered the Distribution Losses Ratio

(DLR). It can be defined as

DLR =total energy losses

total energy demand in feeder(1)

The DLR of case1 is higher than other cases because no

reference scheduling of DGs, BESSs and tap transformers

is considered. The losses are minimized to 8.74% on case 2

because of proposed optimize scheduling. The DLR is reduced

to 7.57% because of curtailment of loads by CLs. The impact

of CLs also confirms that the capacity of BESS is minimized

by 20% as compared to case2. This increases the free space

at substation and cost savings.

J. Appliance Commitment for Household Load Scheduling

The experimental results are discussed in this section. The

simulation starts at 12:00 am and runs for 24 hours.

Day-ahead Energy Price Forecast: For simulations, fore-

casted price signal pf is obtained by adding white noise ǫ tothe actual price signal pn.

pf = pn + ǫ (2)

The real time pricing (RTP) forecasted price signal and actual

price signal are shown in fig. 20 (Blue line presents actual

price while red line shows RTP forecasted price).

As discussed above, Appliance commitment schedule is a

two step scheduling process:

1) Day-ahead scheduling: Fig. 21 shows the sorted RTP

prices in ascending order to obtain a composite price curve,

wc. The calculated price Pc is $12/MWh and to warm the

inlet water in the tank, duration of ON time, Tc, is 9.18h.

Here, two cases are considered. Case1: When users comfort

is not considered. In this case, EWH operation depends on

spot price. If spot price is less than Pc, EWH is turned on

Fig. 20. The RTP prices (blue: actual; red: day-ahead forecast).

otherwise off. The cost price is $0.248 which is lower than

that of Scheme A (no control) or transactive control (Scheme

B). Case2: When comfort constraints are observed. Fig. 22

shows that hot water temperature increases and decreases from

specified range, violating the user comfort. To satisfy the

comfort constraints, the temperature band must be included.

Scenario1: we assume that upper and lower range of tem-

perature is constant over the complete scheduling time (24

hours)

140 ≤ θn ≤ 160 when0 ≤ tn ≤ 24 (3)

where θn is the temperature of hot water at time tn(◦F). It

is clear from fig. 22 that the upper limit is violated at t= 1.466

h. Thus, the time interval is divided into two: [0, 1.466] and

[1.466, 24]. The whole optimization process is carried out for

this second interval. When it is done, violation of temperature

range is verified. This process repeats until no violation occurs.

The resultant cost price is $0.811 and its results are shown in

fig. 24a.

Scenario2: the temperature range varies for different times

of the day.

{

132 ≤ θn ≤ 150 when 0 ≤ tn ≤ 24

142 ≤ θn ≤ 160 Otherwise(4)

Fig. 24b shows that temperature band is not violated when

temperature decreases at noon. The resultant cost is $0.798

which is lower as compared to scenario 2.

Fig. 21. The composite price curve wc (price threshold, Pc) (blue: day-aheadforecast price; red: sorted day-ahead forecast price).

Fig. 22. Hot water temperature without considering users comfort .

2) Real time adjustment: As the actual and forecasted

prices are different from each other(see fig. 20), thus the sorted

actual and sorted forecasting prices would be different. The

difference in prices might be less, however, the deviation of

temperature would be large because of forecasting errors in

prices. To solve this problem, real time adjustment is done (see

this mode in detail in [3]). Fig.11 depicts that temperature level

is maintained within defined limits after adjustment. However

there is a trade-off between comfort constraints and cost. The

cost payment for the adjustment is $0.831.

K. Management and Control of Domestic Smart Grid Tech-

nology

Two cases are studied to evaluate the methodology. First

scenario corresponds to simulation of group of houses using

real heat demand data to check whether it is applicable

for neighbors. Second scenario corresponds to evaluation of

proposed methodology for a house. A set of 39 houses and

their real heat demand has been simulated using three step

approach.

1) Planning: First of all, prediction is made using a

predictor. A prediction for a household is presented in fig. 25.

It is clear that predictor follows the trend in a good manner.

However, some deviations occur due to human nature and

uncertain use of heat. Once prediction is done, the global

planner schedules the on-off time of generators. Fig. 26 depicts

the scheduling of electricity generators. To reach the the

desire production plan, two sub-plans are made: one, using

the predicted demand other using real heat demand. Figure

Fig. 23. Hot water temp.(◦F) considering user’s comfort.(a)uniform temp.(b)time varying temp.

Fig. 24. Hot water temperatures (blue: real-time adjustment; red: day-aheadschedule)

shows that total heat demand is predicted in a good manner

(2 kWh difference). However, the heat demand prediction is

less accurate.

Fig. 25. Heat demand prediction for a household

Fig. 26. Planning using the three-step methodology for 39 houses.

IV. CONCLUSION

In this paper, we presented the survey of some home energy

management techniques which play a helpful role to consume

the electrical energy efficiently. Optimal consumption of power

helps in reduction of peak load demand and PAR. Reduction

in PAR increases sustainability, efficiency and reliability of

the system. In this paper we have discussed and compared

HEM techniques on the basis of economic gain and peak load

reduction. We overviewed the simulated results and discussed

their performance. Our discussion will hopefully motivate the

future researches to make more efficient and user friendly

home energy management systems.

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